Overview

Dataset statistics

Number of variables17
Number of observations11738
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory136.0 B

Variable types

Numeric14
DateTime1
Categorical2

Warnings

tweet has a high cardinality: 11734 distinct values High cardinality
mentions is highly correlated with hashtags and 4 other fieldsHigh correlation
hashtags is highly correlated with mentions and 4 other fieldsHigh correlation
video is highly correlated with mentions and 4 other fieldsHigh correlation
photos is highly correlated with hashtags and 3 other fieldsHigh correlation
urls is highly correlated with mentionsHigh correlation
thumbnail is highly correlated with mentions and 4 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
number of tweets is highly correlated with mentions and 4 other fieldsHigh correlation
mentions is highly correlated with hashtags and 4 other fieldsHigh correlation
hashtags is highly correlated with mentions and 4 other fieldsHigh correlation
video is highly correlated with mentions and 5 other fieldsHigh correlation
photos is highly correlated with mentions and 4 other fieldsHigh correlation
urls is highly correlated with mentions and 2 other fieldsHigh correlation
thumbnail is highly correlated with mentions and 5 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
number of tweets is highly correlated with hashtags and 3 other fieldsHigh correlation
df_index is highly correlated with cashtagsHigh correlation
mentions is highly correlated with cashtagsHigh correlation
hashtags is highly correlated with cashtagsHigh correlation
cashtags is highly correlated with df_index and 2 other fieldsHigh correlation
replies_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
retweets_count is highly correlated with replies_count and 1 other fieldsHigh correlation
likes_count is highly correlated with replies_count and 1 other fieldsHigh correlation
thumbnail is highly correlated with hashtags and 3 other fieldsHigh correlation
likes_count is highly correlated with retweets_count and 1 other fieldsHigh correlation
hashtags is highly correlated with thumbnail and 3 other fieldsHigh correlation
urls is highly correlated with mentionsHigh correlation
price is highly correlated with username and 1 other fieldsHigh correlation
retweets_count is highly correlated with likes_count and 1 other fieldsHigh correlation
username is highly correlated with priceHigh correlation
photos is highly correlated with thumbnail and 3 other fieldsHigh correlation
replies_count is highly correlated with likes_count and 1 other fieldsHigh correlation
video is highly correlated with thumbnail and 3 other fieldsHigh correlation
number of tweets is highly correlated with thumbnail and 4 other fieldsHigh correlation
df_index is highly correlated with priceHigh correlation
mentions is highly correlated with urls and 1 other fieldsHigh correlation
cashtags is highly skewed (γ1 = 27.67940974) Skewed
tweet is uniformly distributed Uniform
mentions has 5780 (49.2%) zeros Zeros
hashtags has 7273 (62.0%) zeros Zeros
cashtags has 11655 (99.3%) zeros Zeros
video has 5100 (43.4%) zeros Zeros
photos has 5477 (46.7%) zeros Zeros
urls has 4334 (36.9%) zeros Zeros
thumbnail has 5100 (43.4%) zeros Zeros
retweets_count has 137 (1.2%) zeros Zeros
percent change has 317 (2.7%) zeros Zeros

Reproduction

Analysis started2021-09-19 15:59:33.350534
Analysis finished2021-09-19 16:00:18.326232
Duration44.98 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct3415
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1150.600699
Minimum0
Maximum3414
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:18.503592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1516
median1015
Q31633.75
95-th percentile2827.15
Maximum3414
Range3414
Interquartile range (IQR)1117.75

Descriptive statistics

Standard deviation794.5884952
Coefficient of variation (CV)0.6905857924
Kurtosis0.08044100403
Mean1150.600699
Median Absolute Deviation (MAD)547
Skewness0.7910189729
Sum13505751
Variance631370.8767
MonotonicityNot monotonic
2021-09-19T12:00:18.704740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8436
 
0.1%
6506
 
0.1%
6386
 
0.1%
6396
 
0.1%
6406
 
0.1%
6416
 
0.1%
6426
 
0.1%
6436
 
0.1%
6446
 
0.1%
6456
 
0.1%
Other values (3405)11678
99.5%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
34141
< 0.1%
34131
< 0.1%
34121
< 0.1%
34111
< 0.1%
34101
< 0.1%
34091
< 0.1%
34081
< 0.1%
34071
< 0.1%
34061
< 0.1%
34051
< 0.1%

date
Date

Distinct3570
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size91.8 KiB
Minimum2016-08-23 09:30:00
Maximum2021-07-20 16:00:00
2021-09-19T12:00:18.860206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:19.012411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM

Distinct11734
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size91.8 KiB
This is great
 
3
Good morning
 
2
Facts
 
2
Journalist Q&amp;A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today
 
1
My latest episode on @SXMInsight with @DMiliband: https://t.co/HHbydG9unD https://t.co/s4pdn31tKT It’s been a pleasure working with @yaniksilver and co on solving problems big and small over the past decade: https://t.co/FTtnyLr7wT @VirginUnite https://t.co/4TQQqKUAEz Welcome to the @Virgin_Orbit team Stanny! https://t.co/Py9TGLRaUV #VirginFamily https://t.co/tEb30o08hW An incredible act of compassion and a powerful image as Brant Jean forgives and embraces the woman who killed his brother: https://t.co/inxrNpgmGB #ReadByRichard https://t.co/5IjfFn8Zoc
 
1
Other values (11729)
11729 

Length

Max length11161
Median length342
Mean length543.682825
Min length1

Characters and Unicode

Total characters6381749
Distinct characters968
Distinct categories23 ?
Distinct scripts12 ?
Distinct blocks31 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11731 ?
Unique (%)99.9%

Sample

1st rowJournalist Q&amp;A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today
2nd row@Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet.
3rd rowThanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhY
4th row@Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh oh
5th rowLoss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon.

Common Values

ValueCountFrequency (%)
This is great3
 
< 0.1%
Good morning2
 
< 0.1%
Facts2
 
< 0.1%
Journalist Q&amp;A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today1
 
< 0.1%
My latest episode on @SXMInsight with @DMiliband: https://t.co/HHbydG9unD https://t.co/s4pdn31tKT It’s been a pleasure working with @yaniksilver and co on solving problems big and small over the past decade: https://t.co/FTtnyLr7wT @VirginUnite https://t.co/4TQQqKUAEz Welcome to the @Virgin_Orbit team Stanny! https://t.co/Py9TGLRaUV #VirginFamily https://t.co/tEb30o08hW An incredible act of compassion and a powerful image as Brant Jean forgives and embraces the woman who killed his brother: https://t.co/inxrNpgmGB #ReadByRichard https://t.co/5IjfFn8Zoc1
 
< 0.1%
“Life isn’t about 'finding' fulfilment and success – it’s about creating it. Why then has creativity been given a back seat in our culture? No longer.” – @ChaseJarvis https://t.co/8ymDrvjWgD #CreativeCalling https://t.co/5gmZWUgnxP One to add to your podcast list this week – this episode of Earth Unscrewed has a fascinating discussion with sustainability scientist Johan Rockström https://t.co/MUuYtNHoKd #podcasts1
 
< 0.1%
Sporting the ocean-inspired t-shirt I designed, one of @OceanUnite’s new range of circular economy t-shirts available on their @Teemillstore. All proceeds go to support their work to rebuild #ocean health and #resilience. https://t.co/sPQKYvwqIK https://t.co/ql9KjWqn7W Over the years I’ve had to make a conscious effort to put my health and fitness first (after all, you only have one body, so it makes sense to try and look after it!) https://t.co/4NtyHBREru1
 
< 0.1%
It’s difficult to imagine how any country can maximise its potential by ignoring significant populations of stateless people. My take on statelessness and how to solve it https://t.co/R5gRApf5pa #IBelong @Refugees https://t.co/SoGgG1rJtb Continued and growing statelessness is an enormous challenge for humanity. But the optimist in me tries to see the upside: when given an identity, a sense of belonging, people will be able to fulfil their true potential https://t.co/R5gRApwGNK #IBelong @Refugees https://t.co/eOlZGydmsS No one should have to suffer the indignity and exclusion that comes with being stateless. My take on a problem affecting millions around the world https://t.co/R5gRApwGNK #IBelong @Refugees https://t.co/QQcJM9spVr1
 
< 0.1%
Wonderful bringing together leaders from organisations @VirginUnite has incubated to strengthen and celebrate the connections between them https://t.co/JFHfetYuVI @VirginUnite @thebteamhq @TheElders @RockyMtnInst @_thenewnow @oceanunite @ccs_accelerator @bigchange_ https://t.co/FLR0NMG3B1 Looking forward to seeing the pitches! When we work closely together in new combinations and collaborations, one plus one can equal three https://t.co/JFHfetYuVI @VirginUnite @thebteamhq @TheElders @RockyMtnInst @_thenewnow @oceanunite @ccs_accelerator @bigchange_ https://t.co/YkzKKADQQf Holly sat down with some businesses that are all about impact to hear their start-up stories: https://t.co/FjlnGuRwnJ @HollyBranson https://t.co/A6aygtSu5J Congratulations @VirginLimitedEd – great to see all four African properties featuring in @CNTraveler’s Readers’ Choice awards 2019 for best hotels &amp; resorts in Africa #Ulusaba #KasbahTamadot #MahaliMzuri #MontRochelle1
 
< 0.1%
Great to see @OneLessBTL are tackling ocean plastic pollution at source – working to transform London into a place where single-use bottled water is a thing of the past: https://t.co/9Pf55goDuL #GoodbyeOceanPlastic #OneLess https://t.co/bif4GPwvKL When brilliant people collaborate on projects to have a positive impact in the world, magic can happen https://t.co/JFHfetYuVI @VirginUnite @thebteamhq @TheElders @RockyMtnInst @_thenewnow @oceanunite @ccs_accelerator @bigchange_ https://t.co/Mg10IxB4xU Exciting news involving @virgingalactic and @Boeing: https://t.co/O9yQrP1QFX https://t.co/zAkcdEl3XK Sustainable investing and diversity is good for business. Here’s the data to prove it: https://t.co/CZ4u4ulciZ #ReadByRichard1
 
< 0.1%
Other values (11724)11724
99.9%

Length

2021-09-19T12:00:19.380896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to25098
 
2.9%
the24839
 
2.8%
a14267
 
1.6%
of12402
 
1.4%
and11795
 
1.3%
you11386
 
1.3%
tmobile11035
 
1.3%
for10138
 
1.2%
is9926
 
1.1%
in9618
 
1.1%
Other values (103850)738118
84.0%

Most occurring characters

ValueCountFrequency (%)
911705
 
14.3%
e481710
 
7.5%
t429934
 
6.7%
o381697
 
6.0%
a328603
 
5.1%
i291106
 
4.6%
s271861
 
4.3%
n270319
 
4.2%
r252987
 
4.0%
l200026
 
3.1%
Other values (958)2561801
40.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4329229
67.8%
Space Separator912063
 
14.3%
Uppercase Letter495181
 
7.8%
Other Punctuation442901
 
6.9%
Decimal Number128960
 
2.0%
Other Symbol19696
 
0.3%
Final Punctuation16636
 
0.3%
Connector Punctuation9487
 
0.1%
Dash Punctuation9168
 
0.1%
Close Punctuation4843
 
0.1%
Other values (13)13585
 
0.2%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
🙌2088
 
10.6%
😂1403
 
7.1%
😉1060
 
5.4%
801
 
4.1%
👍653
 
3.3%
🤣563
 
2.9%
😎396
 
2.0%
🤔364
 
1.8%
🎉360
 
1.8%
334
 
1.7%
Other values (743)11674
59.3%
Lowercase Letter
ValueCountFrequency (%)
e481710
 
11.1%
t429934
 
9.9%
o381697
 
8.8%
a328603
 
7.6%
i291106
 
6.7%
s271861
 
6.3%
n270319
 
6.2%
r252987
 
5.8%
l200026
 
4.6%
h195931
 
4.5%
Other values (50)1225055
28.3%
Uppercase Letter
ValueCountFrequency (%)
T51451
 
10.4%
M38265
 
7.7%
S32451
 
6.6%
I28205
 
5.7%
A25462
 
5.1%
C23244
 
4.7%
W22180
 
4.5%
E19029
 
3.8%
B18658
 
3.8%
H17680
 
3.6%
Other values (36)218556
44.1%
Other Punctuation
ValueCountFrequency (%)
/114055
25.8%
@93795
21.2%
.77691
17.5%
!44038
 
9.9%
:43169
 
9.7%
,20626
 
4.7%
#16437
 
3.7%
?9011
 
2.0%
;8142
 
1.8%
&6632
 
1.5%
Other values (12)9305
 
2.1%
Other Letter
ValueCountFrequency (%)
36
72.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
1
 
2.0%
𖨆1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (5)5
 
10.0%
Math Symbol
ValueCountFrequency (%)
+461
41.6%
~303
27.3%
=277
25.0%
|38
 
3.4%
16
 
1.4%
3
 
0.3%
2
 
0.2%
2
 
0.2%
2
 
0.2%
1
 
0.1%
Other values (4)4
 
0.4%
Decimal Number
ValueCountFrequency (%)
119711
15.3%
018564
14.4%
215944
12.4%
311977
9.3%
511436
8.9%
410807
8.4%
710424
8.1%
910394
8.1%
810227
7.9%
69475
7.3%
Modifier Symbol
ValueCountFrequency (%)
🏼2466
68.3%
🏻952
 
26.4%
¯72
 
2.0%
🏽65
 
1.8%
^17
 
0.5%
🏿17
 
0.5%
16
 
0.4%
´3
 
0.1%
Format
ValueCountFrequency (%)
458
31.3%
431
29.5%
424
29.0%
­146
 
10.0%
2
 
0.1%
1
 
0.1%
Space Separator
ValueCountFrequency (%)
911705
> 99.9%
 349
 
< 0.1%
6
 
< 0.1%
 2
 
< 0.1%
1
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$1702
97.8%
£35
 
2.0%
1
 
0.1%
1
 
0.1%
1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-7220
78.8%
1572
 
17.1%
375
 
4.1%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(1590
98.9%
[16
 
1.0%
{1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
)4826
99.6%
]15
 
0.3%
}2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
15076
90.6%
1559
 
9.4%
»1
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_9472
99.8%
_15
 
0.2%
Initial Punctuation
ValueCountFrequency (%)
1579
90.4%
168
 
9.6%
Nonspacing Mark
ValueCountFrequency (%)
2172
100.0%
Enclosing Mark
ValueCountFrequency (%)
57
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ1
100.0%
Control
ValueCountFrequency (%)
30
100.0%
Other Number
ValueCountFrequency (%)
½1
100.0%
Private Use
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4824341
75.6%
Common1554634
 
24.4%
Inherited2660
 
< 0.1%
Cyrillic39
 
< 0.1%
Katakana36
 
< 0.1%
Braille19
 
< 0.1%
Han6
 
< 0.1%
Hangul6
 
< 0.1%
Greek5
 
< 0.1%
Bamum1
 
< 0.1%
Other values (2)2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
911705
58.6%
/114055
 
7.3%
@93795
 
6.0%
.77691
 
5.0%
!44038
 
2.8%
:43169
 
2.8%
,20626
 
1.3%
119711
 
1.3%
018564
 
1.2%
#16437
 
1.1%
Other values (850)194843
 
12.5%
Latin
ValueCountFrequency (%)
e481710
 
10.0%
t429934
 
8.9%
o381697
 
7.9%
a328603
 
6.8%
i291106
 
6.0%
s271861
 
5.6%
n270319
 
5.6%
r252987
 
5.2%
l200026
 
4.1%
h195931
 
4.1%
Other values (58)1720167
35.7%
Cyrillic
ValueCountFrequency (%)
о9
23.1%
в4
10.3%
К3
 
7.7%
р3
 
7.7%
л3
 
7.7%
д2
 
5.1%
к2
 
5.1%
ё2
 
5.1%
и2
 
5.1%
а1
 
2.6%
Other values (8)8
20.5%
Han
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Inherited
ValueCountFrequency (%)
2172
81.7%
431
 
16.2%
57
 
2.1%
Greek
ValueCountFrequency (%)
Δ4
80.0%
θ1
 
20.0%
Bamum
ValueCountFrequency (%)
𖨆1
100.0%
Katakana
ValueCountFrequency (%)
36
100.0%
Hiragana
ValueCountFrequency (%)
1
100.0%
Unknown
ValueCountFrequency (%)
1
100.0%
Braille
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6331502
99.2%
Punctuation23951
 
0.4%
None12707
 
0.2%
Emoticons7440
 
0.1%
VS2172
 
< 0.1%
Dingbats1503
 
< 0.1%
Misc Symbols1009
 
< 0.1%
Latin 1 Sup682
 
< 0.1%
Enclosed Alphanum Sup549
 
< 0.1%
Misc Technical43
 
< 0.1%
Other values (21)191
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
911705
 
14.4%
e481710
 
7.6%
t429934
 
6.8%
o381697
 
6.0%
a328603
 
5.2%
i291106
 
4.6%
s271861
 
4.3%
n270319
 
4.3%
r252987
 
4.0%
l200026
 
3.2%
Other values (83)2511554
39.7%
Punctuation
ValueCountFrequency (%)
15076
62.9%
2216
 
9.3%
1579
 
6.6%
1572
 
6.6%
1559
 
6.5%
458
 
1.9%
431
 
1.8%
424
 
1.8%
375
 
1.6%
168
 
0.7%
Other values (9)93
 
0.4%
Dingbats
ValueCountFrequency (%)
801
53.3%
334
22.2%
135
 
9.0%
56
 
3.7%
54
 
3.6%
25
 
1.7%
21
 
1.4%
13
 
0.9%
12
 
0.8%
12
 
0.8%
Other values (13)40
 
2.7%
VS
ValueCountFrequency (%)
2172
100.0%
Emoticons
ValueCountFrequency (%)
🙌2088
28.1%
😂1403
18.9%
😉1060
14.2%
😎396
 
5.3%
😊311
 
4.2%
😁283
 
3.8%
😍190
 
2.6%
🙏171
 
2.3%
😀162
 
2.2%
😏154
 
2.1%
Other values (60)1222
16.4%
None
ValueCountFrequency (%)
🏼2466
 
19.4%
🏻952
 
7.5%
👍653
 
5.1%
🤣563
 
4.4%
🤔364
 
2.9%
🎉360
 
2.8%
👏283
 
2.2%
📱255
 
2.0%
👀220
 
1.7%
🔥215
 
1.7%
Other values (571)6376
50.2%
Latin 1 Sup
ValueCountFrequency (%)
 349
51.2%
­146
21.4%
¯72
 
10.6%
£35
 
5.1%
é23
 
3.4%
ö13
 
1.9%
ç8
 
1.2%
ü7
 
1.0%
§7
 
1.0%
°5
 
0.7%
Other values (12)17
 
2.5%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇸92
16.8%
🇺91
16.6%
🇳51
9.3%
🇮38
 
6.9%
🇵37
 
6.7%
🇬29
 
5.3%
🆓29
 
5.3%
🇯27
 
4.9%
🆕22
 
4.0%
🇷18
 
3.3%
Other values (17)115
20.9%
Misc Symbols
ValueCountFrequency (%)
248
24.6%
136
13.5%
114
11.3%
80
 
7.9%
61
 
6.0%
56
 
5.6%
50
 
5.0%
46
 
4.6%
21
 
2.1%
20
 
2.0%
Other values (35)177
17.5%
Currency Symbols
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
CJK
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Latin Ext A
ValueCountFrequency (%)
ō2
33.3%
ā2
33.3%
ē1
16.7%
ū1
16.7%
Math Operators
ValueCountFrequency (%)
2
28.6%
2
28.6%
1
14.3%
1
14.3%
1
14.3%
Letterlike Symbols
ValueCountFrequency (%)
7
77.8%
2
 
22.2%
Cyrillic
ValueCountFrequency (%)
о9
23.1%
в4
10.3%
К3
 
7.7%
р3
 
7.7%
л3
 
7.7%
д2
 
5.1%
к2
 
5.1%
ё2
 
5.1%
и2
 
5.1%
а1
 
2.6%
Other values (8)8
20.5%
Bamum Sup
ValueCountFrequency (%)
𖨆1
100.0%
Sup Arrows B
ValueCountFrequency (%)
3
100.0%
Katakana
ValueCountFrequency (%)
36
100.0%
Misc Technical
ValueCountFrequency (%)
23
53.5%
8
 
18.6%
4
 
9.3%
4
 
9.3%
1
 
2.3%
1
 
2.3%
1
 
2.3%
1
 
2.3%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Arrows
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Geometric Shapes Ext
ValueCountFrequency (%)
🟩1
25.0%
🟠1
25.0%
🟣1
25.0%
🟢1
25.0%
Modifier Letters
ValueCountFrequency (%)
ʻ1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Hiragana
ValueCountFrequency (%)
1
100.0%
Math Alphanum
ValueCountFrequency (%)
𝑬4
16.7%
𝑶2
 
8.3%
𝑰2
 
8.3%
𝑳2
 
8.3%
𝟬1
 
4.2%
𝑻1
 
4.2%
𝑴1
 
4.2%
𝑩1
 
4.2%
𝑿1
 
4.2%
𝑪1
 
4.2%
Other values (8)8
33.3%
Playing Cards
ValueCountFrequency (%)
🃏1
100.0%
Specials
ValueCountFrequency (%)
4
100.0%
PUA
ValueCountFrequency (%)
1
100.0%
Braille
ValueCountFrequency (%)
19
100.0%
Geometric Shapes
ValueCountFrequency (%)
16
100.0%

username
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size91.8 KiB
JohnLegere
3351 
elonmusk
2157 
richardbranson
2009 
Benioff
1836 
jack
1442 

Length

Max length14
Median length8
Mean length8.709064577
Min length4

Characters and Unicode

Total characters102227
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowelonmusk
2nd rowelonmusk
3rd rowelonmusk
4th rowelonmusk
5th rowelonmusk

Common Values

ValueCountFrequency (%)
JohnLegere3351
28.5%
elonmusk2157
18.4%
richardbranson2009
17.1%
Benioff1836
15.6%
jack1442
12.3%
levie943
 
8.0%

Length

2021-09-19T12:00:19.687677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-19T12:00:19.772862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
johnlegere3351
28.5%
elonmusk2157
18.4%
richardbranson2009
17.1%
benioff1836
15.6%
jack1442
12.3%
levie943
 
8.0%

Most occurring characters

ValueCountFrequency (%)
e15932
15.6%
n11362
 
11.1%
r9378
 
9.2%
o9353
 
9.1%
a5460
 
5.3%
h5360
 
5.2%
i4788
 
4.7%
s4166
 
4.1%
f3672
 
3.6%
k3599
 
3.5%
Other values (12)29157
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter93689
91.6%
Uppercase Letter8538
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e15932
17.0%
n11362
12.1%
r9378
10.0%
o9353
10.0%
a5460
 
5.8%
h5360
 
5.7%
i4788
 
5.1%
s4166
 
4.4%
f3672
 
3.9%
k3599
 
3.8%
Other values (9)20619
22.0%
Uppercase Letter
ValueCountFrequency (%)
J3351
39.2%
L3351
39.2%
B1836
21.5%

Most occurring scripts

ValueCountFrequency (%)
Latin102227
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e15932
15.6%
n11362
 
11.1%
r9378
 
9.2%
o9353
 
9.1%
a5460
 
5.3%
h5360
 
5.2%
i4788
 
4.7%
s4166
 
4.1%
f3672
 
3.6%
k3599
 
3.5%
Other values (12)29157
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII102227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e15932
15.6%
n11362
 
11.1%
r9378
 
9.2%
o9353
 
9.1%
a5460
 
5.3%
h5360
 
5.2%
i4788
 
4.7%
s4166
 
4.1%
f3672
 
3.6%
k3599
 
3.5%
Other values (12)29157
28.5%

mentions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.708808996
Minimum0
Maximum53
Zeros5780
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:19.883110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum53
Range53
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.905496684
Coefficient of variation (CV)1.700305119
Kurtosis24.7553221
Mean1.708808996
Median Absolute Deviation (MAD)1
Skewness3.660959306
Sum20058
Variance8.441910978
MonotonicityNot monotonic
2021-09-19T12:00:20.021498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
05780
49.2%
11920
 
16.4%
21296
 
11.0%
3837
 
7.1%
4554
 
4.7%
5393
 
3.3%
6251
 
2.1%
7193
 
1.6%
8120
 
1.0%
9103
 
0.9%
Other values (22)291
 
2.5%
ValueCountFrequency (%)
05780
49.2%
11920
 
16.4%
21296
 
11.0%
3837
 
7.1%
4554
 
4.7%
5393
 
3.3%
6251
 
2.1%
7193
 
1.6%
8120
 
1.0%
9103
 
0.9%
ValueCountFrequency (%)
531
 
< 0.1%
381
 
< 0.1%
361
 
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
272
 
< 0.1%
251
 
< 0.1%
247
0.1%
237
0.1%
222
 
< 0.1%

hashtags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.374936105
Minimum0
Maximum42
Zeros7273
Zeros (%)62.0%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:20.159333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum42
Range42
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.79836894
Coefficient of variation (CV)2.035271988
Kurtosis23.03208099
Mean1.374936105
Median Absolute Deviation (MAD)0
Skewness3.757956869
Sum16139
Variance7.830868723
MonotonicityNot monotonic
2021-09-19T12:00:20.281305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
07273
62.0%
11437
 
12.2%
2882
 
7.5%
3557
 
4.7%
4396
 
3.4%
5311
 
2.6%
6228
 
1.9%
7156
 
1.3%
8122
 
1.0%
985
 
0.7%
Other values (23)291
 
2.5%
ValueCountFrequency (%)
07273
62.0%
11437
 
12.2%
2882
 
7.5%
3557
 
4.7%
4396
 
3.4%
5311
 
2.6%
6228
 
1.9%
7156
 
1.3%
8122
 
1.0%
985
 
0.7%
ValueCountFrequency (%)
421
< 0.1%
361
< 0.1%
332
< 0.1%
322
< 0.1%
311
< 0.1%
301
< 0.1%
291
< 0.1%
271
< 0.1%
261
< 0.1%
241
< 0.1%

cashtags
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02036121997
Minimum0
Maximum16
Zeros11655
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:20.393194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3596470097
Coefficient of variation (CV)17.66333305
Kurtosis931.864852
Mean0.02036121997
Median Absolute Deviation (MAD)0
Skewness27.67940974
Sum239
Variance0.1293459716
MonotonicityNot monotonic
2021-09-19T12:00:20.504091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
011655
99.3%
144
 
0.4%
211
 
0.1%
39
 
0.1%
64
 
< 0.1%
44
 
< 0.1%
73
 
< 0.1%
82
 
< 0.1%
151
 
< 0.1%
101
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
011655
99.3%
144
 
0.4%
211
 
0.1%
39
 
0.1%
44
 
< 0.1%
51
 
< 0.1%
64
 
< 0.1%
73
 
< 0.1%
82
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
161
 
< 0.1%
151
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
101
 
< 0.1%
82
< 0.1%
73
< 0.1%
64
< 0.1%
51
 
< 0.1%
44
< 0.1%

video
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.62267848
Minimum0
Maximum36
Zeros5100
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:20.627829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum36
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.280601971
Coefficient of variation (CV)1.405455239
Kurtosis11.87105571
Mean1.62267848
Median Absolute Deviation (MAD)1
Skewness2.498875148
Sum19047
Variance5.201145351
MonotonicityNot monotonic
2021-09-19T12:00:20.749100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
05100
43.4%
12394
20.4%
21389
 
11.8%
3996
 
8.5%
4667
 
5.7%
5417
 
3.6%
6266
 
2.3%
7198
 
1.7%
8103
 
0.9%
964
 
0.5%
Other values (14)144
 
1.2%
ValueCountFrequency (%)
05100
43.4%
12394
20.4%
21389
 
11.8%
3996
 
8.5%
4667
 
5.7%
5417
 
3.6%
6266
 
2.3%
7198
 
1.7%
8103
 
0.9%
964
 
0.5%
ValueCountFrequency (%)
361
 
< 0.1%
251
 
< 0.1%
211
 
< 0.1%
202
 
< 0.1%
192
 
< 0.1%
182
 
< 0.1%
174
< 0.1%
161
 
< 0.1%
156
0.1%
148
0.1%

photos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.66314534
Minimum0
Maximum25
Zeros5477
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:20.884318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum25
Range25
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.436412536
Coefficient of variation (CV)1.464942647
Kurtosis7.884243427
Mean1.66314534
Median Absolute Deviation (MAD)1
Skewness2.317843759
Sum19522
Variance5.936106047
MonotonicityNot monotonic
2021-09-19T12:00:21.009063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
05477
46.7%
12104
 
17.9%
21200
 
10.2%
3906
 
7.7%
4706
 
6.0%
5445
 
3.8%
6292
 
2.5%
7220
 
1.9%
8126
 
1.1%
986
 
0.7%
Other values (12)176
 
1.5%
ValueCountFrequency (%)
05477
46.7%
12104
 
17.9%
21200
 
10.2%
3906
 
7.7%
4706
 
6.0%
5445
 
3.8%
6292
 
2.5%
7220
 
1.9%
8126
 
1.1%
986
 
0.7%
ValueCountFrequency (%)
251
 
< 0.1%
213
 
< 0.1%
205
 
< 0.1%
183
 
< 0.1%
175
 
< 0.1%
166
 
0.1%
157
 
0.1%
149
 
0.1%
1325
0.2%
1218
0.2%

urls
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.566195263
Minimum0
Maximum32
Zeros4334
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:21.128896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.947313757
Coefficient of variation (CV)1.243340343
Kurtosis15.07779571
Mean1.566195263
Median Absolute Deviation (MAD)1
Skewness2.511473577
Sum18384
Variance3.792030867
MonotonicityNot monotonic
2021-09-19T12:00:21.650592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
04334
36.9%
12951
25.1%
21768
15.1%
31074
 
9.1%
4666
 
5.7%
5395
 
3.4%
6246
 
2.1%
7167
 
1.4%
860
 
0.5%
926
 
0.2%
Other values (12)51
 
0.4%
ValueCountFrequency (%)
04334
36.9%
12951
25.1%
21768
15.1%
31074
 
9.1%
4666
 
5.7%
5395
 
3.4%
6246
 
2.1%
7167
 
1.4%
860
 
0.5%
926
 
0.2%
ValueCountFrequency (%)
321
 
< 0.1%
242
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
191
 
< 0.1%
171
 
< 0.1%
167
0.1%
151
 
< 0.1%
134
< 0.1%
123
< 0.1%

thumbnail
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.62267848
Minimum0
Maximum36
Zeros5100
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:21.799767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum36
Range36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.280601971
Coefficient of variation (CV)1.405455239
Kurtosis11.87105571
Mean1.62267848
Median Absolute Deviation (MAD)1
Skewness2.498875148
Sum19047
Variance5.201145351
MonotonicityNot monotonic
2021-09-19T12:00:21.922195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
05100
43.4%
12394
20.4%
21389
 
11.8%
3996
 
8.5%
4667
 
5.7%
5417
 
3.6%
6266
 
2.3%
7198
 
1.7%
8103
 
0.9%
964
 
0.5%
Other values (14)144
 
1.2%
ValueCountFrequency (%)
05100
43.4%
12394
20.4%
21389
 
11.8%
3996
 
8.5%
4667
 
5.7%
5417
 
3.6%
6266
 
2.3%
7198
 
1.7%
8103
 
0.9%
964
 
0.5%
ValueCountFrequency (%)
361
 
< 0.1%
251
 
< 0.1%
211
 
< 0.1%
202
 
< 0.1%
192
 
< 0.1%
182
 
< 0.1%
174
< 0.1%
161
 
< 0.1%
156
0.1%
148
0.1%

replies_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2104
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1083.109303
Minimum0
Maximum204414
Zeros100
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:22.070052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q127
median78
Q3236
95-th percentile3891.6
Maximum204414
Range204414
Interquartile range (IQR)209

Descriptive statistics

Standard deviation5788.531288
Coefficient of variation (CV)5.344364849
Kurtosis296.0133894
Mean1083.109303
Median Absolute Deviation (MAD)64
Skewness14.07556516
Sum12713537
Variance33507094.47
MonotonicityNot monotonic
2021-09-19T12:00:22.228040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3164
 
1.4%
5154
 
1.3%
2152
 
1.3%
6139
 
1.2%
1137
 
1.2%
12132
 
1.1%
4119
 
1.0%
8119
 
1.0%
9118
 
1.0%
7117
 
1.0%
Other values (2094)10387
88.5%
ValueCountFrequency (%)
0100
0.9%
1137
1.2%
2152
1.3%
3164
1.4%
4119
1.0%
5154
1.3%
6139
1.2%
7117
1.0%
8119
1.0%
9118
1.0%
ValueCountFrequency (%)
2044141
< 0.1%
1517751
< 0.1%
1445341
< 0.1%
1178941
< 0.1%
1118541
< 0.1%
1043341
< 0.1%
1024551
< 0.1%
896881
< 0.1%
885441
< 0.1%
861261
< 0.1%

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2627
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2762.580508
Minimum0
Maximum582467
Zeros137
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:22.387663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q153
median149
Q3439
95-th percentile11525.85
Maximum582467
Range582467
Interquartile range (IQR)386

Descriptive statistics

Standard deviation14467.5629
Coefficient of variation (CV)5.236974219
Kurtosis346.7118513
Mean2762.580508
Median Absolute Deviation (MAD)121
Skewness14.24994382
Sum32427170
Variance209310376.2
MonotonicityNot monotonic
2021-09-19T12:00:22.536630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0137
 
1.2%
199
 
0.8%
391
 
0.8%
288
 
0.7%
479
 
0.7%
2775
 
0.6%
2466
 
0.6%
1963
 
0.5%
563
 
0.5%
662
 
0.5%
Other values (2617)10915
93.0%
ValueCountFrequency (%)
0137
1.2%
199
0.8%
288
0.7%
391
0.8%
479
0.7%
563
0.5%
662
0.5%
754
 
0.5%
860
0.5%
962
0.5%
ValueCountFrequency (%)
5824671
< 0.1%
3771801
< 0.1%
2957021
< 0.1%
2799021
< 0.1%
2758731
< 0.1%
2380141
< 0.1%
2196201
< 0.1%
2189131
< 0.1%
2026971
< 0.1%
1985331
< 0.1%

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5092
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26086.11424
Minimum0
Maximum4727301
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:22.690342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61.85
Q1390
median971.5
Q32815.75
95-th percentile126599
Maximum4727301
Range4727301
Interquartile range (IQR)2425.75

Descriptive statistics

Standard deviation120644.8036
Coefficient of variation (CV)4.624866798
Kurtosis259.0093154
Mean26086.11424
Median Absolute Deviation (MAD)734.5
Skewness11.58416872
Sum306198809
Variance1.455516865 × 1010
MonotonicityNot monotonic
2021-09-19T12:00:22.841024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1519
 
0.2%
23617
 
0.1%
13315
 
0.1%
1715
 
0.1%
515
 
0.1%
615
 
0.1%
315
 
0.1%
13515
 
0.1%
23414
 
0.1%
9814
 
0.1%
Other values (5082)11584
98.7%
ValueCountFrequency (%)
06
 
0.1%
19
0.1%
27
0.1%
315
0.1%
410
0.1%
515
0.1%
615
0.1%
714
0.1%
89
0.1%
914
0.1%
ValueCountFrequency (%)
47273011
< 0.1%
20405341
< 0.1%
18272501
< 0.1%
17164591
< 0.1%
16591461
< 0.1%
16269531
< 0.1%
16006071
< 0.1%
15828261
< 0.1%
15747231
< 0.1%
15715041
< 0.1%

number of tweets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.683591753
Minimum1
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:22.989351image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile20
Maximum162
Range161
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.062947174
Coefficient of variation (CV)1.242690798
Kurtosis42.53977616
Mean5.683591753
Median Absolute Deviation (MAD)2
Skewness4.079101092
Sum66714
Variance49.88522278
MonotonicityNot monotonic
2021-09-19T12:00:23.147827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13143
26.8%
21953
16.6%
31270
10.8%
4924
 
7.9%
5710
 
6.0%
6580
 
4.9%
7473
 
4.0%
8323
 
2.8%
10272
 
2.3%
9254
 
2.2%
Other values (49)1836
15.6%
ValueCountFrequency (%)
13143
26.8%
21953
16.6%
31270
10.8%
4924
 
7.9%
5710
 
6.0%
6580
 
4.9%
7473
 
4.0%
8323
 
2.8%
9254
 
2.2%
10272
 
2.3%
ValueCountFrequency (%)
1621
< 0.1%
1331
< 0.1%
1111
< 0.1%
971
< 0.1%
661
< 0.1%
611
< 0.1%
591
< 0.1%
571
< 0.1%
562
< 0.1%
531
< 0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8848
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.83139474
Minimum7.190000057
Maximum891.3800049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size91.8 KiB
2021-09-19T12:00:23.309418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum7.190000057
5-th percentile10.02999973
Q121.73499966
median61.65500069
Q394.46499825
95-th percentile258.6534882
Maximum891.3800049
Range884.1900048
Interquartile range (IQR)72.72999859

Descriptive statistics

Standard deviation124.0068076
Coefficient of variation (CV)1.380439522
Kurtosis15.30565764
Mean89.83139474
Median Absolute Deviation (MAD)37.94500065
Skewness3.685889727
Sum1054440.911
Variance15377.68833
MonotonicityNot monotonic
2021-09-19T12:00:23.455123image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1059
 
0.5%
10.0500001955
 
0.5%
10.0399999654
 
0.5%
10.0600004248
 
0.4%
10.0299997344
 
0.4%
9.9399995837
 
0.3%
9.97000026732
 
0.3%
9.96000003827
 
0.2%
9.94999980927
 
0.2%
10.0799999224
 
0.2%
Other values (8838)11331
96.5%
ValueCountFrequency (%)
7.1900000571
 
< 0.1%
7.219999793
< 0.1%
7.252
< 0.1%
7.2600002292
< 0.1%
7.2850000861
 
< 0.1%
7.3000001911
 
< 0.1%
7.3133333521
 
< 0.1%
7.3200001721
 
< 0.1%
7.3299999241
 
< 0.1%
7.3400001531
 
< 0.1%
ValueCountFrequency (%)
891.38000491
< 0.1%
883.09002691
< 0.1%
880.02001951
< 0.1%
870.34997561
< 0.1%
869.66998291
< 0.1%
869.41333011
< 0.1%
861.44665531
< 0.1%
858.02667241
< 0.1%
857.07668051
< 0.1%
8561
< 0.1%

percent change
Real number (ℝ)

ZEROS

Distinct11086
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0008214729126
Minimum-0.1795004093
Maximum0.2690450286
Zeros317
Zeros (%)2.7%
Negative5398
Negative (%)46.0%
Memory size91.8 KiB
2021-09-19T12:00:23.608057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1795004093
5-th percentile-0.02550002682
Q1-0.005426339969
median0.0003730236163
Q30.006739700593
95-th percentile0.02740150081
Maximum0.2690450286
Range0.4485454379
Interquartile range (IQR)0.01216604056

Descriptive statistics

Standard deviation0.01999743622
Coefficient of variation (CV)24.3433909
Kurtosis19.86526611
Mean0.0008214729126
Median Absolute Deviation (MAD)0.006054803716
Skewness0.8883885413
Sum9.642449048
Variance0.0003998974554
MonotonicityNot monotonic
2021-09-19T12:00:23.763968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0317
 
2.7%
-0.000994058495513
 
0.1%
-0.0009950476319
 
0.1%
-0.0009960387378
 
0.1%
0.0009960387377
 
0.1%
0.0010010239357
 
0.1%
-0.0019900952627
 
0.1%
0.0010040390417
 
0.1%
0.0019940636397
 
0.1%
0.0009950476316
 
0.1%
Other values (11076)11350
96.7%
ValueCountFrequency (%)
-0.17950040931
< 0.1%
-0.17880929861
< 0.1%
-0.17599998141
< 0.1%
-0.17084823851
< 0.1%
-0.15573926331
< 0.1%
-0.1536458671
< 0.1%
-0.14380711391
< 0.1%
-0.133235721
< 0.1%
-0.1325104521
< 0.1%
-0.12969874341
< 0.1%
ValueCountFrequency (%)
0.26904502861
< 0.1%
0.23246124651
< 0.1%
0.21480703161
< 0.1%
0.20214456821
< 0.1%
0.19397801351
< 0.1%
0.18753113181
< 0.1%
0.17154858691
< 0.1%
0.16942058651
< 0.1%
0.16775248081
< 0.1%
0.16621153971
< 0.1%

Interactions

2021-09-19T11:59:50.535055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:50.696530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:50.829028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:50.968035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:51.105467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:51.238746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:51.369708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:51.502021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:51.664209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:51.812891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:51.950665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:52.079481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:52.218246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:52.344998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:52.474847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:52.601746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:52.725179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:52.847409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:52.976591image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:53.101280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:53.220112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:53.341403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:53.466408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:53.599247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:53.725000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:53.843635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:53.974315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:54.092654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:54.212595image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:54.335049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:54.452814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:54.565802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:54.689464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:54.811152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:54.939489image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:55.059491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:55.180446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:55.308647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:55.431473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:55.548965image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:55.676249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:55.796321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:55.914915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:56.053115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:56.184363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:56.312981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:56.452191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:56.591027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:56.722735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:56.857128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:56.997007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:57.149710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:57.288004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:57.418769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:57.560185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:57.690756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:57.822450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:57.958648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:58.087033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:58.211952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:58.346666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:58.479839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:58.607483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:58.736676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:58.869505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:59.010277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:59.180538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:59.381916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:59.576966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:59.747639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T11:59:59.915828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:00.081869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:00.201638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:00.318540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:00.450101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:00.574511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:00.693420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:00.817885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:00.963823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:01.096556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:01.223600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:01.342213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:02.495665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:02.613081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:02.739702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:02.871783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:02.996618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:03.117692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:03.248715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:03.376361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:03.500961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:03.626318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:03.755873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:03.891318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:04.022522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:04.144055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:04.276509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:04.397978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:04.522038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:04.657926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:04.786802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:04.922999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:05.062279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:05.194374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:05.321789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:05.451266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:05.585161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:05.726072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:05.860873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:05.987637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:06.131016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:06.264046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:06.391128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:06.533411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:06.670858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:06.813893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:06.955963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:07.098752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:07.232803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:07.371214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:07.513800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:07.662250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:07.809649image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:07.943027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:08.089222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:08.221612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:08.368032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:08.502237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:08.630409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:08.754873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:08.889694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:09.021995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:09.148257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:09.276040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:09.407541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:09.547238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:09.687721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:09.813170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:09.950370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:10.077277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:10.202620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:10.326967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:10.443513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:10.559410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:10.684720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:10.807335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:10.937912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:11.371659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:11.493951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:11.629637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:11.763848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:11.889976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:12.017322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:12.136339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:12.255514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:12.419704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:12.552318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:12.683247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:12.824195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:12.962506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:13.107367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:13.242879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:13.382660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:13.527528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:13.667082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:13.798482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:13.940285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:14.072021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:14.204289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:14.326031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:14.441676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:14.554353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:14.677491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:14.797327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:14.912136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:15.029742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:15.152302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:15.289399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:15.410299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:15.524817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:15.648953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:15.760459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:15.875484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:16.000517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:16.122662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:16.238436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:16.367023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:16.491126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:16.624986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:16.746042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:16.870353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:17.000596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:17.127497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:17.244525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:17.378337image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-19T12:00:17.497478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-19T12:00:23.920276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-19T12:00:24.159409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-19T12:00:24.383939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-19T12:00:24.609884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-19T12:00:17.778786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-19T12:00:18.151276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexdatetweetusernamementionshashtagscashtagsvideophotosurlsthumbnailreplies_countretweets_countlikes_countnumber of tweetspricepercent change
0222016-08-23 16:00:00Journalist Q&amp;A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time todayelonmusk0000000872464013965244.9679990.002318
1232016-08-28 09:30:00@Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet.elonmusk000000068137789244.1626660.011081
2242016-08-30 16:00:00Thanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhYelonmusk000001014218397353142.268002-0.022072
3252016-08-31 16:00:00@Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh ohelonmusk0000000391202110165542.4020000.007508
4262016-09-01 16:00:00Loss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon.elonmusk00000001159484810743140.153999-0.039424
5272016-09-02 09:30:00@scrappydog yes. This seems instant from a human perspective, but it really a fast fire, not an explosion. Dragon would have been fine. Finishing Autopilot blog postponed to end of weekendelonmusk00000004265883702240.4660000.007770
6282016-09-09 09:30:00Will get back to Autopilot update blog tomorrow. @ashwin7002 @NASA @faa @AFPAA We have not ruled that out. @LewisChandlerDN nope, it wasn't me Particularly trying to understand the quieter bang sound a few seconds before the fireball goes off. May come from rocket or something else. Support &amp; advice from @NASA, @FAA, @AFPAA &amp; others much appreciated. Please email any recordings of the event to report@spacex.com. Important to note that this happened during a routine filling operation. Engines were not on and there was no apparent heat source. Still working on the Falcon fireball investigation. Turning out to be the most difficult and complex failure we have ever had in 14 years. @waitbutwhy It's been a little crazy latelyelonmusk30000001761575220453839.8180010.008766
7292016-09-09 16:00:00@abadcliche Most likely true, but we can't yet find it on any vehicle sensorselonmusk10000003123189138.894001-0.023206
8302016-09-10 09:30:00Thoughtful Op-ed in Space News much appreciated https://t.co/CJq5g3NIEKelonmusk000001013814003698139.5453340.016746
9312016-09-10 16:00:00Will do some press Q&amp;A on Autopilot post at 11am PDT tmrw and then publish at noon. Sorry about delay. Unusually difficult couple of weeks.elonmusk00000003218455376139.149334-0.010014

Last rows

df_indexdatetweetusernamementionshashtagscashtagsvideophotosurlsthumbnailreplies_countretweets_countlikes_countnumber of tweetspricepercent change
1172834052021-07-15 16:00:00Big congratulations to @Candace_Parker! Can’t wait to buy! https://t.co/QNmxa35kMR See you never, Fleets! https://t.co/6bATAKXEvlJohnLegere1000020651062148.339996-0.006696
1172934062021-07-16 09:30:00Congratulations to @neilbarua and the @ServiceMax team!!! https://t.co/AFaIqM1I48 More additions to Clubhouse! Who still actively uses??! https://t.co/OCwsKhscIvJohnLegere200002038452148.089996-0.001685
1173034072021-07-16 16:00:00Trust science! It’s important to continue staying safe, the COVID-19 pandemic isn’t over yet! https://t.co/HBhDZCbvrk BUT WHERE IS THE SOLID PINK HEART?!! https://t.co/wF8LWtG6dxJohnLegere000002036221612149.4100040.008914
1173134082021-07-17 09:30:00Another step forward for accessibility on Twitter. https://t.co/kiC4DpDOL2JohnLegere000001018221147.893331-0.010151
1173234092021-07-17 16:00:00I finally found a way to go to the beach without having to go ON the beach (except to 🏊 in ocean of course) 🌞 🌊 https://t.co/IPXvQZVijB But how will I know when I’m going to get scammed?!! https://t.co/L0slvP3HPG My kind of charcuterie board! https://t.co/6BhW2V747Z Happy #WorldEmojiDay! Which is your favorite?? https://t.co/Px8HHWYOvEJohnLegere010123145243404147.810003-0.000563
1173334102021-07-18 16:00:00Who’s subscribing?? https://t.co/bZr4a84P2X Hey New Yorkers – need an officiant?! https://t.co/PbBecmjgu0 is it possible that the ocean makes the morning coffee ☕️ even better than it's normal great? 😊 https://t.co/43jhIroufdJohnLegere00011211412712183146.210002-0.010066
1173434112021-07-19 09:30:00Number 16 is a movie night game changer!! https://t.co/9YJS0H383PJohnLegere000001052321147.5000000.008823
1173534122021-07-19 16:00:00Gorgeous! Maybe I should charter a magenta rocket. https://t.co/jx2ZL0gX6Q So I'm about to go for a run on the beach.....am i the only one that doesn't think these ☁️ mean 🌧 ☔️ is coming? https://t.co/fMxzuOZ0nF Always love a good Quesarito https://t.co/KaiMmXEc5q What I’ve learned from @Lifehacker is that I do EVERYTHING wrong! https://t.co/xAbzXxZhqZJohnLegere100123125172364144.610001-0.019593
1173634132021-07-20 09:30:00Call on John is POSTPONED for today but tune in on Thursday, July 22nd at 12 PM ET on Instagram Stories for an extra special Call on John! What do you want to see me give away?? https://t.co/4P3hucQ3ioJohnLegere000110173181144.7899930.001245
1173734142021-07-20 16:00:00WOW this looks so delicious! https://t.co/hnOKodchlg Up up and away! https://t.co/jvRcpkqng3JohnLegere00000202891152144.399994-0.002694